10 RUCHO v. COMMON CAUSE KAGAN, J., dissenting tional districting requirements). The effect is to make gerrymanders far more effective and durable than before, insulating politicians against all but the most titanic shifts in the political tides. These are not your grandfather’s—let alone the Framers’—gerrymanders. The proof is in the 2010 pudding. That redistricting cycle produced some of the most extreme partisan gerrymanders in this country’s history. I’ve already recounted the results from North Carolina and Maryland, and you’ll hear even more about those. See supra, at 4–6; infra, at 19–20. But the voters in those States were not the only ones to fall prey to such districting perversions. Take Pennsylvania. In the three congressional elections occurring under the State’s original districting plan (before the State Supreme Court struck it down), Democrats received between 45% and 51% of the statewide vote, but won only 5 of 18 House seats. See League of Women Voters v. Pennsylvania, ___ Pa. ___, ___, 178 A. 3d 737, 764 (2018). Or go next door to Ohio. There, in four congressional elections, Democrats tallied between 39% and 47% of the statewide vote, but never won more than 4 of 16 House seats. See Ohio A. Philip Randolph Inst. v. Householder, 373 F. Supp. 3d 978, 1074 (SD Ohio 2019). (Nor is there any reason to think that the results in those States stemmed from political geography or non-partisan districting criteria, rather than from partisan manipulation. See infra, at 15, 31.) And gerrymanders will only get worse (or depending on your perspective, better) as time goes on—as data becomes ever more fine-grained and data analysis techniques continue to improve. What was possible with paper and pen—or even with Windows 95—doesn’t hold a candle (or an LED bulb?) to what will become possible with developments like machine learning. And someplace along this road, “we the people” become sovereign no longer.